Climate Impact Visualizer (CIV)

Advanced AI-Driven Environmental Monitoring & Strategic Risk Analytics

🌍 Project Overview

The Climate Impact Visualizer is a high-performance analytical platform designed to bridge the gap between complex meteorological telemetry and actionable environmental intelligence. Developed as a comprehensive resilience framework, the system synthesizes multi-source data to provide real-time monitoring, 30-year longitudinal historical reconstruction, and AI-powered predictive risk assessments for flood, drought, and thermal stress.

🚀 Key Features

1. Geospatial Intelligence Engine

• Real-Time Overlays: Dynamic Leaflet-based mapping integration providing spatial visualization of precipitation, wind velocity, and temperature gradients.
• Localized Telemetry: High-precision coordinate-based analysis utilizing reverse-geocoding for granular regional insights.

2. AI Predictive Risk Analytics

• Sensor Fusion Modeling: Custom-trained algorithms analyze the convergence of soil moisture, atmospheric aridity (VPD), and precipitation patterns.
• Probabilistic Forecasting: Generates 24-hour risk scores (High/Medium/Low) with associated confidence intervals for flood and drought events.
• Thermal Vulnerability: Calculates regional heat vulnerability based on peak temperature thresholds and night-time recovery potential.

3. Longitudinal Historical Deep-Dive

• 30-Year Evolution: Reconstructs climate shifts since 1993 using high-resolution historical re-analysis.
• Statistical Indices: Implementation of the Standardized Precipitation Index (SPI) to quantify moisture deficits and identify multi-year drought cycles.
• Trend Identification: Uses linear regression to isolate significant warming trends and precipitation anomalies.

4. Strategic Adaptation Framework

• Actionable Intelligence: Automated generation of mitigation protocols (Infrastructure Prep, Resource Allocation, and Safety Advisories) based on real-time risk levels.
• Resilience Reports: Tools for generating regional environmental summaries to assist in urban and agricultural planning.

🛠 Technical Architecture

Frontend Stack

• React.js: Component-based UI architecture for high-responsiveness.
• Chart.js / React-Chartjs-2: Advanced data visualization for projection trends and historical climatology.
• Leaflet & React-Leaflet: Geospatial rendering engine for atmospheric overlays.
• Bootstrap 5: Professional, mobile-responsive layout and KPI dashboarding.

Data & API Integrations

• Open-Meteo: Primary source for terrestrial soil moisture and historical re-analysis.
• OpenWeatherMap: Real-time atmospheric tile server and global weather telemetry.
• Nominatim (OSM): Advanced geospatial geocoding and reverse-search logic.
• AQI Services: Real-time monitoring of PM2.5, NO₂, and O₃ concentrations.

📈 Methodology

The system utilizes a Heuristic Risk Weighting approach combined with Linear Regression for trend analysis.

• Flood Risk: Evaluated via a combination of soil saturation percentage and peak hourly precipitation volume.
• Drought Risk: Calculated through an Aridity Index incorporating Vapour Pressure Deficit (VPD) and persistent moisture deficits.
• Heat Stress: Measured against a localized 95th percentile threshold derived from the 30-year historical baseline of the specific coordinate.

📦 Installation & Setup

Prerequisites

• Node.js (v16.x or higher)
• npm or yarn
• API Keys for OpenWeatherMap (Required for map layers)

Environment Configuration

Create a .env file in the frontend directory:

REACT_APP_OWM_API_KEY=your_api_key_here

Launching the Frontend

cd frontend
npm install
npm start

🛡 Disclaimer

This project was developed as a Final Year Project. The predictive models are probabilistic and intended for research and educational purposes. Always consult local emergency management agencies during extreme weather events.